Aiming at the problems of complex background information and small target of unmanned vehicle-borne optical images in marine environment， insufficient feature extraction ability， weak positioning ability and poor detection accuracy of the current target detection algorithm， an improved maritime target detection algorithm based on YOLOv7-Tiny is proposed. The feature extraction module RepELAN is designed by using the "lossless" feature of RepVGG during inference， which improves the feature extraction capability of the network without affecting the inference speed. The feature sharing and fusion network is improved， which fuses high-resolution feature maps to improve the ability to extract features of small targets， and crops low-resolution feature maps to reduce the amount of network inference calculation. Aiming at the problem that the network has weak positioning and detection capabilities in complex environments， the detection head module is designed to distinguish between two decoupling heads， positioning and classification， and improve the network positioning detection capability. In the established ship target detection dataset， the detection accuracy is improved by 6.2%， and the module ablation experiment and comparative experiment are designed， which demonstrates the effectiveness of the proposed algorithm.
